A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

Lean Yu (), Lihang Yu and Kaitao Yu
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Lihang Yu: Beijing University of Chemical Technology
Kaitao Yu: Canada International School of Beijing

Financial Innovation, 2021, vol. 7, issue 1, 1-20

Abstract: Abstract To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100

Keywords: High dimensionality; Trait-driven learning paradigm; Feature extraction; Classifier selection; Credit risk classification (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1186/s40854-021-00249-x

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